Cleaning Big Data Streams: A Systematic Literature Review

Alotaibi, Obaid; Pardede, Eric; Tomy, Sarath

doi:10.3390/technologies11040101

Cited by 5 publications

(3 citation statements)

References 86 publications

(104 reference statements)

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“…Ref. [7] explores the broader context of cleaning big data streams, emphasizing the challenges posed by continuous data generation and the limitations of traditional data cleaning methods. They highlight the importance of addressing common issues in data cleaning, including missing values, duplicated data, outliers, and irrelevant data, within the context of big data streams.…”

Section: Literature Reviewmentioning

confidence: 99%

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Seasonal Data Cleaning for Sales with Chase Demand Strategy

Malindzakova,

Malindzak,

Kubik

2024

Standards

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The intricate process of planning production, involving product life cycle management and the synthesis of manufacturing information, is crucial for coherence in manufacturing. Manufacturing companies, operating in a high-mix, low-volume production environment, integrate production planning with management to focus on production processes, emphasizing high-quality, rapid product delivery. This includes material item planning to anticipate future demands and ensure sufficient raw material and finished product quantities, considering purchasing, production, and sales capacities. This study explores the electro technical sector, specifically a manufacturing entity specializing in low-voltage plastic cable distribution boxes. It scrutinizes the vital role of seasonal data cleaning in optimizing production planning, with a targeted focus on three products. The implementation of a chase demand strategy is related to capacity planning, taking into account the change in production capacity linked to demand over time. The problem in implementing this strategy is related to the fluctuating level of quality due to changes in demand for specified products.

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Section: Literature Reviewmentioning

confidence: 99%

“…Ref. [7] emphasizes the importance of real-time data cleaning in managing large volumes of data, particularly in high-mix, low-volume production environments. Their insights address challenges in data analysis, aligning with the precision needed for production planning amidst structural changes and data quality issues.…”

Section: Literature Reviewmentioning

confidence: 99%

Seasonal Data Cleaning for Sales with Chase Demand Strategy

Malindzakova,

Malindzak,

Kubik

2024

Standards

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“…Methodologies utilized to assess the efficacy of these treatments were also discovered [49]. Missing values, duplicated data, outliers, and irrelevant data were listed as cleaning concerns that may arise throughout the cleaning process.…”

Section: Cleaning Datamentioning

confidence: 99%

A Model for Enhancing Unstructured Big Data Warehouse Execution Time

Farhan,

Youssef,

Abdelhamid

2024

BDCC

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Traditional data warehouses (DWs) have played a key role in business intelligence and decision support systems. However, the rapid growth of the data generated by the current applications requires new data warehousing systems. In big data, it is important to adapt the existing warehouse systems to overcome new issues and limitations. The main drawbacks of traditional Extract–Transform–Load (ETL) are that a huge amount of data cannot be processed over ETL and that the execution time is very high when the data are unstructured. This paper focuses on a new model consisting of four layers: Extract–Clean–Load–Transform (ECLT), designed for processing unstructured big data, with specific emphasis on text. The model aims to reduce execution time through experimental procedures. ECLT is applied and tested using Spark, which is a framework employed in Python. Finally, this paper compares the execution time of ECLT with different models by applying two datasets. Experimental results showed that for a data size of 1 TB, the execution time of ECLT is 41.8 s. When the data size increases to 1 million articles, the execution time is 119.6 s. These findings demonstrate that ECLT outperforms ETL, ELT, DELT, ELTL, and ELTA in terms of execution time.

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Can regulated technological FOMO be used to enhance technology adoption at the bottom of the pyramid?

Joshi

2024

JSTPM

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Purpose This study aims to focus on exploring the role of fear of missing out (FOMO) in the technology adoption context, whereby the bottom-of-the-pyramid (BOP) segment was studied to explore the factors responsible for the development of behavioural intentions (BI) to use unified payments interface (UPI), a disruptive technological phenomenon in the mobile payment systems field. Design/methodology/approach A mixed-method research approach involving both qualitative and quantitative methods was used. Initially, qualitative data obtained through interviews with UPI’s BOP users were subjected to thematic analysis, leading to the identification of eight factors and the construction of a conceptual model. Subsequently, 354 responses were gathered, and empirical analysis was conducted using structural equation modelling in AMOS 23.0. Findings Eight factors, including personal and social benefits, perceived security risk, socio-cultural influences, governmental influence, usability, psychological inertia, perceived value (PV) and FOMO, were discovered. The quantitative examination confirmed the validity of the conceptual model in the BOP context, explaining 51% of the variance in BI. FOMO and PV emerged as robust indicators of UPI adoption, with PV significantly regulating the impact of FOMO. Originality/value To the best of the author’s knowledge, this study is one of the first to explore what drives BOP users in an emerging economy to adopt UPI. The conceptual model it presents contributes to the advancement of technology adoption literature by incorporating FOMO alongside newly identified BOP-specific factors.

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Cleaning Big Data Streams: A Systematic Literature Review

Cited by 5 publications

References 86 publications

Seasonal Data Cleaning for Sales with Chase Demand Strategy

Seasonal Data Cleaning for Sales with Chase Demand Strategy

A Model for Enhancing Unstructured Big Data Warehouse Execution Time

Can regulated technological FOMO be used to enhance technology adoption at the bottom of the pyramid?

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